Traditionally, contact centers facilitate the receipt, response and routing of incoming customer communications such as telephone calls, emails, and instant messaging sessions relating to customer service, retention, and sales. Generally, a customer is in contact with a customer service representative (“CSR”), also referred to as a contact center agent, who is responsible for answering the customer's inquiries and/or directing the customer to the appropriate individual, department, information source, or service as required to satisfy the customer's needs. Further, upon contacting a contact center, the customer may also enter into an automated self-service system such as, for example, an interactive voice response (“IVR”) system. In an IVR system, the customer interacts with the IVR system directly to conserve human resources.
Often, contact centers monitor calls between a customer and the CSR. Accordingly, contact centers sometimes employ individuals responsible for listening to the conversation between the customer and the agent. Many companies have in-house call centers to respond to customer complaints and inquiries. While monitoring of such calls may occur in real time, it is often more efficient and useful to record the call for later review and other purposes. Information gathered from such calls is typically used to monitor the performance of the call center agents to identify possible training needs. Based on the subsequent review and analysis of the conversation, a monitor can make suggestions or recommendations to improve the quality of the customer interaction. Additionally, in contact center environments, it may be common to collect relevant data relating to customers, such as biographical data and purchase history data.
Conventionally, collected demographic data may be used to predict the likelihood of certain outcomes, such as whether customers will purchase specific items. Because customers are interacting with CSRs through new and different communication channels, data collected about a customer and data recorded during a customer interaction is becoming more diverse. As a result, predictions about customer actions have become less accurate. Further, outcome predictions are often based on data collected during a single interaction and thus may not be as accurate as they could be with respect to customers in identifiable groups. Accordingly, while existing contact center prediction systems and methods have been generally adequate for their intended purposes, they have not been entirely satisfactory in all respects. The apparatuses and methods described herein advantageously may overcome one or more of the deficiencies in conventional systems.